gather with rtweet

library(tidyverse)
library(rtweet)

Search tweets

search_tweets

• @nprmusic
• #tinydesk
• @RhiannonGiddens [porgy & bess]

{r}
bts <- search_tweets("#BTS", n = 5000, include_rts = FALSE)

bts_dynamite <- search_tweets("#BTS dynamite", n = 5000, include_rts = FALSE)

bts

bts_dynamite

get friends

@john_little

john_little <-  get_friends("john_little")

john_little

john_little_data <- lookup_users(john_little$user_id)

john_little_data

get followers

jrl_flw <- get_followers("john_little")

jrl_flw_data <- lookup_users(jrl_flw$user_id)

jrl_flw_data 

timelines

rg_tmls <- get_timelines("RhiannonGiddens", n = 3200)

min(created_at)	max(created_at)
2015-04-21 13:59:55	2020-10-27 20:49:18

rg_tmls %>% 
  dplyr::filter(created_at > "2016-01-01") %>%  
  dplyr::group_by(screen_name) %>%
  ts_plot("weeks", trim = 1L) +
  ggplot2::geom_point() +
  geom_smooth(se = FALSE, color = "cadetblue") +
  colorblindr::scale_color_OkabeIto() +
  hrbrthemes::theme_ipsum(grid = "Y") +
  ggplot2::theme(
    legend.title = ggplot2::element_blank(),
    legend.position = "bottom", 
    plot.title = ggplot2::element_text(face = "bold")
    ) +
    ggplot2::labs(
    x = NULL, y = NULL,
    title = "Frequency of Twitter statuses",
    subtitle = "Twitter status (tweet) counts aggregated by week from Jan. 2016",
    caption = "Source: Data collected from Twitter's REST API via rtweet"
  )

NA
NA

get_favorites

rg_faves <- get_favorites("RhiannonGiddens", n = 3000)

rg_faves

search users

gullah <- search_users("#gullah", n = 1000)

Searching for users...
Finished collecting users!

gullah

get trends

# sf <- get_trends("san franciso")
# durham <- get_trends(lat = 36.0, lng = -78.9)
greensboro <- get_trends("greensboro")

greensboro